In the original IM problem, the topological structure of the underlying social network is assumed to be fully available [1]}. Despite the growing popularity of studies on IM, such an assumption is hardly realistic as the underlying true network is not initially known in various domains of real-world network applications [2]}, [3]}. Although we can reconstruct the complete network so that existing IM algorithms may be applied afterwards, retrieving relationships among nodes in a network is very labor-intensive and thus collecting a complete network structure is often considered impractical [2]}. To overcome this problem, one could alternatively leverage social network data to infer unknown relationships. However, even with the increasing availability of social media platforms, this source of information is not without limitations such as restricted resources and privacy settings specified by users. For example, 52.6% of Facebook users hid their friends in a demographic analysis of Facebook in New York City in June 2011 [5]}. Since June 2019, Twitter has begun limiting access to its Application Programming Interface (API) to 100,000 requests per day,https://developer.twitter.com. which may hamper the acquisition of a complete sample of the Twitter network. Moreover, such connectivity information often includes many weak links, which are not effective maximizing the influence spread, e.g., users may have hundreds of friends on Facebook whom they barely know [6]}. Consequently, we are able to observe only a part of the network structure in many practical situations (see [7]}, [8]}, [9]} and references therein).
